Towards Explainable AI Planning as a Service
This work addresses the need for explainability in AI planning systems, though it is incremental as it builds on existing planning methods without introducing a fundamentally new paradigm.
The paper tackles the problem of making AI planning more interpretable by proposing a service-based wrapper that uses existing planners to answer contrastive questions, with a prototype framework developed to demonstrate this approach.
Explainable AI is an important area of research within which Explainable Planning is an emerging topic. In this paper, we argue that Explainable Planning can be designed as a service -- that is, as a wrapper around an existing planning system that utilises the existing planner to assist in answering contrastive questions. We introduce a prototype framework to facilitate this, along with some examples of how a planner can be used to address certain types of contrastive questions. We discuss the main advantages and limitations of such an approach and we identify open questions for Explainable Planning as a service that identify several possible research directions.